Instructions to use RoseRudolph/rudy-nemo-12b-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RoseRudolph/rudy-nemo-12b-v1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="RoseRudolph/rudy-nemo-12b-v1",
	filename="Rudy-Nemo-12B-v1-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use RoseRudolph/rudy-nemo-12b-v1 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf RoseRudolph/rudy-nemo-12b-v1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RoseRudolph/rudy-nemo-12b-v1:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf RoseRudolph/rudy-nemo-12b-v1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RoseRudolph/rudy-nemo-12b-v1:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf RoseRudolph/rudy-nemo-12b-v1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RoseRudolph/rudy-nemo-12b-v1:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf RoseRudolph/rudy-nemo-12b-v1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RoseRudolph/rudy-nemo-12b-v1:Q4_K_M

Use Docker

docker model run hf.co/RoseRudolph/rudy-nemo-12b-v1:Q4_K_M

LM Studio
Jan

vLLM

How to use RoseRudolph/rudy-nemo-12b-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RoseRudolph/rudy-nemo-12b-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RoseRudolph/rudy-nemo-12b-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RoseRudolph/rudy-nemo-12b-v1:Q4_K_M

Ollama
How to use RoseRudolph/rudy-nemo-12b-v1 with Ollama:
```
ollama run hf.co/RoseRudolph/rudy-nemo-12b-v1:Q4_K_M
```

Unsloth Studio

How to use RoseRudolph/rudy-nemo-12b-v1 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for RoseRudolph/rudy-nemo-12b-v1 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for RoseRudolph/rudy-nemo-12b-v1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for RoseRudolph/rudy-nemo-12b-v1 to start chatting

How to use RoseRudolph/rudy-nemo-12b-v1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf RoseRudolph/rudy-nemo-12b-v1:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "RoseRudolph/rudy-nemo-12b-v1:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use RoseRudolph/rudy-nemo-12b-v1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf RoseRudolph/rudy-nemo-12b-v1:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default RoseRudolph/rudy-nemo-12b-v1:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use RoseRudolph/rudy-nemo-12b-v1 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf RoseRudolph/rudy-nemo-12b-v1:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "RoseRudolph/rudy-nemo-12b-v1:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use RoseRudolph/rudy-nemo-12b-v1 with Docker Model Runner:
```
docker model run hf.co/RoseRudolph/rudy-nemo-12b-v1:Q4_K_M
```

Lemonade

How to use RoseRudolph/rudy-nemo-12b-v1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RoseRudolph/rudy-nemo-12b-v1:Q4_K_M

Run and chat with the model

lemonade run user.rudy-nemo-12b-v1-Q4_K_M

List all available models

lemonade list

Rudy Nemo 12B v1 (GGUF)

Rudy Nemo 12B v1 is a SLERP merge designed to combine the strong instruction following, long-context stability, and coherent reasoning of Mistral NeMo with the expressive dialogue and character writing of Rocinante. The goal is a model that stays consistent over long roleplay sessions while producing vivid prose and natural conversations.

This repository provides quantized GGUF files (Q4_K_M) ready to run locally in tools like LM Studio, Ollama, standard llama.cpp, and Jan.ai.

Recommended Generation Settings

To get the absolute best storytelling and creative writing performance out of this model, use these tested, optimal sampler configurations:

Temperature: 1.2
Min P: 0.08
Top P: 0.95
Repetition Penalty: 1.07
Context Size: 8192 (or higher depending on your hardware)
Top K: 0 (Disabled)
Typical P / TFS / Top A: 0 (Disabled)

slerp victims

This model was built using Mergekit via Slerp to combine the instruction-following precision of Mistral NeMo with the narrative qualities of Rocinante

Base Model: mistralai/Mistral-Nemo-Instruct-2407
Secondary Model: TheDrummer/Rocinante-12B-v1.1

Looking for unquantized files?
RoseRudolph/rudy-nemo-12b-v1-unquantized

You can find this model on Ollama via

**Ollama Run RoseRudolph/rudy-nemo-12b-v1

mergekit config

slices:
  - sources:
      - model: mistralai/Mistral-Nemo-Instruct-2407
        layer_range: [0, 40]
      - model: TheDrummer/Rocinante-12B-v1.1
        layer_range: [0, 40]

merge_method: slerp
base_model: mistralai/Mistral-Nemo-Instruct-2407
tokenizer_source: base

parameters:
  t:
    - filter: self_attn
      value: [0.00, 0.25, 0.50, 0.75, 1.00]
    - filter: mlp
      value: [0.00, 0.25, 0.50, 0.75, 1.00]
    - value: 0.50

dtype: bfloat16

Downloads last month: 32

GGUF

Model size

12B params

Architecture

llama

Hardware compatibility

4-bit

Model tree for RoseRudolph/rudy-nemo-12b-v1

Base model

TheDrummer/Rocinante-12B-v1.1

Quantized

(10)

this model

Collection including RoseRudolph/rudy-nemo-12b-v1

Rudy's Collection

Collection

A collection of Rudy language models built for roleplay, storytelling, and creative writing. Includes official releases, updates, and future iteratio • 2 items • Updated 1 day ago